Solvent Sites Improve Docking Performance of Protein-Protein Complexes and Protein-Protein Interface-Targeted Drugs.

Protein-protein interactions (PPIs) are essential, and modulating their function through PPI-targeted drugs is an important research field. PPI sites are shallow protein surfaces readily accessible to the solvent, thus lacking a proper pocket to fit a drug, while their lack of endogenous ligands prevents drug design by chemical similarity. The development of PPI-blocking compounds is, therefore, a tough challenge. Mixed solvent molecular dynamics has been shown to reveal protein-ligand interaction hot spots in protein active sites by identifying solvent sites (SSs). Furthermore, our group has shown that SSs significantly improve protein-ligand docking. In the present work, we extend our analysis to PPI sites. In particular, we analyzed water, ethanol, and phenol-derived sites in terms of their capacity to predict protein-drug and protein-protein interactions. Subsequently, we show how this information can be incorporated to improve both protein-ligand and protein-protein docking. Finally, we highlight the presence of aromatic clusters as key elements of the corresponding interactions.

Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors.

Computer-aided drug discovery methods play a major role in the development of therapeutically important small molecules, but their performance needs to be improved. Molecular dynamics simulations in mixed solvents are useful in understanding protein-ligand recognition and improving molecular docking predictions. In this work, we used ethanol as a cosolvent to find relevant interactions for ligands toward protein kinase G, an essential protein of Mycobacterium tuberculosis (Mtb). We validated the hot spots by screening a database of fragment-like compounds and another one of known kinase inhibitors. Next, we performed a pharmacophore-guided docking simulation and found three low micromolar inhibitors, including one with a novel chemical scaffold that we expanded to four derivative compounds. Binding affinities were characterized by intrinsic fluorescence quenching assays, isothermal titration calorimetry, and the analysis of melting curves. The predicted binding mode was confirmed by X-ray crystallography. Finally, the compounds significantly inhibited the viability of Mtb in infected THP-1 macrophages.

Specificity and Reactivity of Mycobacterium tuberculosis Serine/Threonine Kinases PknG and PknB.

Mycobacterium tuberculosis (Mtb), the causative agent of Tuberculosis, has 11 eukaryotic-like serine/threonine protein kinases, which play essential roles in cell growth, signal transduction, and pathogenesis. Protein kinase G (PknG) regulates the carbon and nitrogen metabolism by phosphorylation of the glycogen accumulation regulator (GarA) protein at Thr21. Protein kinase B (PknB) is involved in cell wall synthesis and cell shape, as well as phosphorylates GarA but at Thr22. While PknG seems to be constitutively activated and recognition of GarA requires phosphorylation in its unstructured tail, PknB activation is triggered by phosphorylation of its activation loop, which allows binding of the forkhead-associated domain of GarA. In the present work, we used molecular dynamics and quantum-mechanics/molecular mechanics simulations of the catalytically competent complex and kinase activity assays to understand PknG/PknB specificity and reactivity toward GarA. Two hydrophobic residues in GarA, Val24 and Phe25, seem essential for PknG binding and allow specificity for Thr21 phosphorylation. On the other hand, phosphorylated residues in PknB bind Arg26 in GarA and regulate its specificity for Thr22. We also provide a detailed analysis of the free energy profile for the phospho-transfer reaction and show why PknG has a constitutively active conformation not requiring priming phosphorylation in contrast to PknB. Our results provide new insights into these two key enzymes relevant for Mtb and the mechanisms of serine/threonine phosphorylation in bacteria.

Structural basis for the Pr-Pfr long-range signaling mechanism of a full-length bacterial phytochrome at the atomic level.

Phytochromes constitute a widespread photoreceptor family that typically interconverts between two photostates called Pr (red light­absorbing) and Pfr (far-red light­absorbing). The lack of full-length structures solved at the (near-)atomic level in both pure Pr and Pfr states leaves gaps in the structural mechanisms involved in the signal transmission pathways during the photoconversion. Here, we present the crystallographic structures of three versions from the plant pathogen Xanthomonas campestris virulence regulator XccBphP bacteriophytochrome, including two full-length proteins, in the Pr and Pfr states. The structures show a reorganization of the interaction networks within and around the chromophore-binding pocket, an α-helix/ß-sheet tongue transition, and specific domain reorientations, along with interchanging kinks and breaks at the helical spine as a result of the photoswitching, which subsequently affect the quaternary assembly. These structural findings, combined with multidisciplinary studies, allow us to describe the signaling mechanism of a full-length bacterial phytochrome at the atomic level.

eSPC: an online data-analysis platform for molecular biophysics.

All biological processes rely on the formation of protein-ligand, protein-peptide and protein-protein complexes. Studying the affinity, kinetics and thermodynamics of binding between these pairs is critical for understanding basic cellular mechanisms. Many different technologies have been designed for probing interactions between biomolecules, each based on measuring different signals (fluorescence, heat, thermophoresis, scattering and interference, among others). Evaluation of the data from binding experiments and their fitting is an essential step towards the quantification of binding affinities. Here, user-friendly online tools to analyze biophysical data from steady-state fluorescence spectroscopy, microscale thermophoresis and differential scanning fluorimetry experiments are presented. The modules of the data-analysis platform (https://spc.embl-hamburg.de/) contain classical thermodynamic models and clear user guidelines for the determination of equilibrium dissociation constants (Kd) and thermal unfolding parameters such as melting temperatures (Tm).

Deamidation drives molecular aging of the SARS-CoV-2 spike protein receptor-binding motif.

The spike protein is the main protein component of the SARS-CoV-2 virion surface. The spike receptor-binding motif mediates recognition of the human angiotensin-converting enzyme 2 receptor, a critical step in infection, and is the preferential target for spike-neutralizing antibodies. Posttranslational modifications of the spike receptor-binding motif have been shown to modulate viral infectivity and host immune response, but these modifications are still being explored. Here we studied asparagine deamidation of the spike protein, a spontaneous event that leads to the appearance of aspartic and isoaspartic residues, which affect both the protein backbone and its charge. We used computational prediction and biochemical experiments to identify five deamidation hotspots in the SARS-CoV-2 spike protein. Asparagine residues 481 and 501 in the receptor-binding motif deamidate with a half-life of 16.5 and 123 days at 37 °C, respectively. Deamidation is significantly slowed at 4 °C, indicating a strong dependence of spike protein molecular aging on environmental conditions. Deamidation of the spike receptor-binding motif decreases the equilibrium constant for binding to the human angiotensin-converting enzyme 2 receptor more than 3.5-fold, yet its high conservation pattern suggests some positive effect on viral fitness. We propose a model for deamidation of the full SARS-CoV-2 virion illustrating how deamidation of the spike receptor-binding motif could lead to the accumulation on the virion surface of a nonnegligible chemically diverse spike population in a timescale of days. Our findings provide a potential mechanism for molecular aging of the spike protein with significant consequences for understanding virus infectivity and vaccine development.

FoldAffinity: binding affinities from nDSF experiments.

Differential scanning fluorimetry (DSF) using the inherent fluorescence of proteins (nDSF) is a popular technique to evaluate thermal protein stability in different conditions (e.g. buffer, pH). In many cases, ligand binding increases thermal stability of a protein and often this can be detected as a clear shift in nDSF experiments. Here, we evaluate binding affinity quantification based on thermal shifts. We present four protein systems with different binding affinity ligands, ranging from nM to high µM. Our study suggests that binding affinities determined by isothermal analysis are in better agreement with those from established biophysical techniques (ITC and MST) compared to apparent Kds obtained from melting temperatures. In addition, we describe a method to optionally fit the heat capacity change upon unfolding ([Formula: see text]) during the isothermal analysis. This publication includes the release of a web server for easy and accessible application of isothermal analysis to nDSF data.

Six novel variants in the PKLR gene associated with pyruvate kinase deficiency in Argentinian patients.

BACKGROUND: Pyruvate kinase deficiency (PKD) is a rare recessive congenital hemolytic anemia caused by mutations in the PKLR gene. The disease shows a marked variability in clinical expression. We studied the molecular features of nine unrelated Argentinian patients with congenital hemolytic anemia associated with erythrocyte pyruvate kinase deficiency. DESIGN AND METHODS: Routine hematologic investigations were performed to rule out other causes of chronic hemolytic anemia. Sanger sequencing and in-sílico analysis were carried out to identify and characterize the genetics variants. RESULTS: Six different novel missense variants were detected among the 18 studied alleles: c.661 G > C (Asp221His), c.956 G > T (Gly319Val), c.1595 G > C (Arg532Pro), c.347 G > A (Arg116Gln), c.1232 G > T (Gly411Val), c.1021G > A (Gly341Ser). Structural implications of amino-acid substitutions were correlated with the clinical phenotypes seen in the probands. CONCLUSIONS: This is the first comprehensive report on molecular characterization of pyruvate kinase deficiency in Argentina and the second from South America that would contribute to our knowledge on the distribution and frequency of PKLR variants in our population but also offer new insights into the interpretation of the effect of PKLR variants and phenotype.

New one-pot synthesis of anti-tuberculosis compounds inspired on isoniazid.

A library of thirty N-substituted tosyl N'-acryl-hydrazones was prepared with p-toluenesulfonyl hydrazide, methyl propiolate and different aldehydes in a one-pot synthesis via an aza-Michael reaction. The scope of the reaction was studied, including aliphatic, isoprenylic, aromatic and carbocyclic aldehydes. The prepared collection was tested against Mycobacterium tuberculosis H37Rv. Nine analogs of the collection showed Minimum Inhibitory Concentration ≤10 µM, of which the most active members (MIC of 1.25 µM) were exclusively E isomers. In order to validate the mechanism of action of the most active acrylates, we tested their activity on a M. tuberculosis InhA over-expressing strain obtaining MIC that consistently doubled those obtained on the wild type strain. Additionally, the binding mode of those analogs on M. tuberculosis InhA was investigated by docking simulations. The results displayed a hydrogen bond interaction between the sulfonamide and Ile194 and the carbonyl of the methyl ester with Tyr 158 (both critical residues in the interaction with the fatty acyl chain substrate), where the main differences on the binding mode relays on the hydrophobicity of the nitrogen substituent. Additionally, chemoinformatic analysis was performed to evaluate in silico possible cytotoxicity risk and ADME-Tox profile. Based on their simple preparation and interesting antimycobacterial activity profile, the newly prepared aza-acrylates are promising candidates for antitubercular drug development.

Conformational and Reaction Dynamic Coupling in Histidine Kinases: Insights from Hybrid QM/MM Simulations.

Histidine kinases (HK) of bacterial two-component systems represent a hallmark of allosterism in proteins, being able to detect a signal through the sensor domain and transmit this information through the protein matrix to the kinase domain which, once active, autophosphorylates a specific histidine residue. Inactive-to-active transition results in a large conformational change that moves the kinase on top of the histidine. In the present work, we use several molecular simulation techniques (Molecular Dynamics, Hybrid QM/MM, and constant pH molecular dynamics) to study the activation and autophosphorylation reactions in L. plantarum WalK, a cis-acting HK. In agreement with previous results, we show that the chemical step requires tight coupling with the conformational step in order to maintain the histidine phosphoacceptor in the correct tautomeric state, with a reactive δ-nitrogen. During the conformational transition, the kinase domain is never released and walks along the HK helix axis, breaking and forming several conserved residue-based contacts. The phosphate transfer reaction is concerted in the transition state region and is catalyzed through the stabilization of the negative developing charge of transferring phosphate along the reaction.

Aromatic clusters in protein-protein and protein-drug complexes.

Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein-drug and protein-protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids (Trp, Phe, Tyr and His) as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein-protein and protein-drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein-drug and protein-protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein-protein and protein-drug complexes, and suggest that protein-protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein-drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein-drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules.

Kinase Activation by Small Conformational Changes.

Protein kinases (PKs) are allosteric enzymes that play an essential role in signal transduction by regulating a variety of key cellular processes. Most PKs suffer conformational rearrangements upon phosphorylation that strongly enhance the catalytic activity. Generally, it involves the movement of the phosphorylated loop toward the active site and the rotation of the whole C-terminal lobe. However, not all kinases undergo such a large configurational change: The MAPK extracellular signal-regulated protein kinases ERK1 and ERK2 achieve a 50 000 fold increase in kinase activity with only a small motion of the C-terminal region. In the present work, we used a combination of molecular simulation tools to characterize the conformational landscape of ERK2 in the active (phosphorylated) and inactive (unphosphorylated) states in solution in agreement with NMR experiments. We show that the chemical reaction barrier is strongly dependent on ATP conformation and that the "active" low-barrier configuration is subtly regulated by phosphorylation, which stabilizes a key salt bridge between the conserved Lys52 and Glu69 belonging to helix-C and promotes binding of a second Mg ion. Our study highlights that the on-off switch embedded in the kinase fold can be regulated by small, medium, and large conformational changes.

Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening.

Virtual screening of large compound databases, looking for potential ligands of a target protein, is a major tool in computer-aided drug discovery. Throughout the years, different techniques such as similarity searching, pharmacophore matching, or molecular docking have been applied with the aim of finding hit compounds showing appreciable affinity. Molecular dynamics simulations in mixed solvents have been shown to identify hot spots relevant for protein-drug interaction, and implementations based on this knowledge were developed to improve pharmacophore matching of small molecules, binding free-energy estimations, and docking performance in terms of pose prediction. Here, we proved in a retrospective manner that cosolvent-derived pharmacophores from molecular dynamics (solvent sites) improve the performance of docking-based virtual screening campaigns. We applied a biased docking scheme based on solvent sites to nine relevant target proteins that have a set of known ligands or actives and compounds that are, presumably, nonbinders (decoys). Our results show improvement in virtual screening performance compared to traditional docking programs both at a global level, with up to 35% increase in areas under the receiver operating characteristic curve, and in early stages, with up to a 7-fold increase in enrichment factors at 1%. However, the improvement in pose prediction of actives was less profound. The presented application makes use of the AutoDock Bias method and is the only cosolvent-derived pharmacophore technique that employs its knowledge both in the ligand conformational search algorithm and the final affinity scoring for virtual screening purposes.

AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions.

SUMMARY: The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. AVAILABILITY AND IMPLEMENTATION: AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Solvents to Fragments to Drugs: MD Applications in Drug Design.

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.

Multiscale approach to the activation and phosphotransfer mechanism of CpxA histidine kinase reveals a tight coupling between conformational and chemical steps.

Sensor histidine kinases (SHKs) are an integral component of the molecular machinery that permits bacteria to adapt to widely changing environmental conditions. CpxA, an extensively studied SHK, is a multidomain homodimeric protein with each subunit consisting of a periplasmic sensor domain, a transmembrane domain, a signal-transducing HAMP domain, a dimerization and histidine phospho-acceptor sub-domain (DHp) and a catalytic and ATP-binding subdomain (CA). The key activation event involves the rearrangement of the HAMP-DHp helical core and translation of the CA towards the acceptor histidine, which presumably results in an autokinase-competent complex. In the present work we integrate coarse-grained, all-atom, and hybrid QM-MM computer simulations to probe the large-scale conformational reorganization that takes place from the inactive to the autokinase-competent state (conformational step), and evaluate its relation to the autokinase reaction itself (chemical step). Our results highlight a tight coupling between conformational and chemical steps, underscoring the advantage of CA walking along the DHp core, to favor a reactive tautomeric state of the phospho-acceptor histidine. The results not only represent an example of multiscale modelling, but also show how protein dynamics can promote catalysis.

Structural and mechanistic comparison of the Cyclopropane Mycolic Acid Synthases (CMAS) protein family of Mycobacterium tuberculosis.

Tuberculosis (TB) is a chronic disease caused by the bacillus Mycobacterium tuberculosis(Mtb) and remains a leading cause of mortality worldwide. The bacteria has an external wall which protects it from being killed, and the enzymes involved in the biosynthesis of the cell wall components have been proposed as promising targets for future drug development efforts. Cyclopropane Mycolic Acid Synthases (CMAS) constitute a group of ten homologous enzymes which belong to the mycolic acid biosynthesis pathway. These enzymes have S-adenosyl-l-methionine (SAM) dependent methyltransferase activity with a peculiarity, each one of them has strong substrate selectivity and reaction specificity, being able to produce among other things cyclopropanes or methyl-alcohol groups from the lipid olefin group. How each CMAS processes its substrate and how the specificity and selectivity are encoded in the protein sequence and structure, is still unclear. In this work, by using a combination of modeling tools, including comparative modeling, docking, all-atom MD and QM/MM methodologies we studied in detail the reaction mechanism of cmaA2, mmaA4, and mmaA1 CMAS and described the molecular determinants that lead to different products. We have modeled the protein-substrate complex structure and determined the free energy pathway for the reaction. The combination of modeling tools at different levels of complexity allows having a complete picture of the CMAS structure-activity relationship.

Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 µs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.